A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

Adverse events following immunization associated with the ChAdOx1nCoV-19 and BBV152 vaccine-a cross-sectional study

COVID-19, an infectious disease, has become a leading cause of death in many people. The rapid emergence of the pandemic prompted the development of a vaccine to mitigate the disease's harmful consequences. Vaccination is the only effective way to prevent infection from spreading and build immunity to the virus. However, developing adverse effects has become a major problem for vaccine reluctance. Accordingly, the interest has been shifted towards identifying the adverse effects developed following immunization. The current study objective is to assess and compare the intensity of adverse effects following 1st and 2nd dose of COVID-19 vaccination and the medication administered to relieve the symptoms associated with vaccination. A cross-sectional study was performed in a community over six months. A total of 836 participants were involved in the study. All the data regarding the vaccination were collected through a specially designed questionnaire form and analyzed in all the participants within the study group. According to the study, at least 1 AEFI was developed in about 90% of the study population. The most common systemic and local effect developed in the study population was fever (59.42%) and pain at the injection site (69.82%), respectively. With both vaccines (ChAdOx1 nCoV-19 and BBV152), the incidence and severity of AEFIs were lower after the second dose than after the first dose, and most of the symptoms associated with vaccination were alleviated by taking home remedies and symptomatic treatment. The adverse effects reported after receiving the ChAdOx1 nCoV-19 and BBV152 vaccines are typical of most vaccines, and the majority of them were tolerated, and most subsided in less than 24 hours.

Research progress on the regulation of endothelial cells by natural products of traditional Chinese medicine in the prevention and treatment of atherosclerosis

[] Atherosclerosis (As) is the pathological basis of coronary heart disease, and vascular endothelial injury is the initiating factor of coronary atherosclerosis. Vascular endothelial cells are a single layer of cells located in the inner layer of blood vessels and regulates exchanges between the blood stream and the surrounding tissues, and their integrity is very important. Many active monomers and the derivatives in natural products of traditional Chinese medicine modulate the function of endothelial cells by intervening oxidative stress, regulating the release of vasoactive substances, reducing inflammation, and equilibrating coagulation and anticoagulant system. They have the advantages of multi-pathway, multi-link and multi-target regulation in protecting from endothelial injury and attenuating atherogenesis. They have also been used to protect against corona virus disease 2019 (COVID-19) induced endothelial injury and atheroslerosis. This article reviews the research progress of the above issues in this field. © 2023, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.

Body composition and physical activity of university students during COVID-19 lockdowns.

[Purpose] Behavioral restrictions during the corona virus disease 2019 (COVID-19) pandemic may have affected the physical activity levels of college students. We aimed to characterize the body composition and physical activity of college students during these behavioral restrictions. [Participants and Methods] The body composition (height, weight, body mass index, body fat mass, body fat percentage, total body muscle mass, free-fat muscle index [FFMI], and fat mass index [FMI]), physical activity, amount the of walking, amount of daily activity, and the number of steps were measured in 52 university students. [Results] For both male and females, the number of steps taken was lower than the average steps reported by the Ministry of Health, Labour and Welfare. In males, FFMI had a strong positive correlation with physical activity, amount of walking, and the number of steps taken. In females, FFMI had a strong positive correlation with physical activity and the amount of walking, as well as a moderate positive correlation with the amount of daily activity. [Conclusion] Since physical activity and walking of university students during COVID-19 affect FFMI, it is necessary to develop an exercise program that considers behavioral patterns.

[Interpretation of the 2021 American Academy of Allergy, Asthma and Immunology expert consensus on the treatment of postinfectious olfactory dysfunction].

Respiratory tract viruses are the second leading cause of olfactory dysfunction. Between 2019 to 2022, the world has been plagued by the problem of olfaction caused by the COVID-19. As we learn more about the impact of severe acute respiratory syndrome coronavirus 2（SARS-CoV-2）, with the recognition that olfactory dysfunction is a key symptom of this disease process, there is a greater need than ever for evidence-based management of postinfectious olfactory dysfunction（PIOD）. The Clinical Olfactory Working Group has proposed theconsensus on the roles of PIOD. This paper is the detailed interpretation of the consensus.

Time to Extubation among ARDS Subjects with and without COVID-19 Pneumonia.

BACKGROUND: Pneumonia from COVID-19 that results in ARDS may require invasive mechanical ventilation. This retrospective study assessed the characteristics and outcomes of subjects with COVID-19-associated ARDS versus ARDS (non-COVID) during the first 6 months of the COVID-19 pandemic in 2020. The primary objective was to determine whether mechanical ventilation duration differed between these cohorts and identify other potential contributory factors. METHODS: We retrospectively identified 73 subjects admitted between March 1 and August 12, 2020, with either COVID-19-associated ARDS (37) or ARDS (36) who were managed with the lung protective ventilator protocol and required > 48 h of mechanical ventilation. Exclusion criteria were the following: <18 years old or the patient required tracheostomy or interfacility transfer. Demographic and baseline clinical data were collected at ARDS onset (ARDS day 0), with subsequent data collected on ARDS days 1-3, 5, 7, 10, 14, and 21. Comparisons were made by using the Wilcoxon rank-sum test (continuous variables) and chi-square test (categorical variables) stratified by COVID-19 status. A Cox proportional hazards model assessed the cause-specific hazard ratio for extubation. RESULTS: The median (interquartile range) mechanical ventilation duration among the subjects who survived to extubation was longer in those with COVID-19-ARDS versus the subjects with non-COVID ARDS: 10 (6-20) d versus 4 (2-8) d; P < .001. Hospital mortality was not different between the two groups (22% vs 39%; P = .11). The competing risks Cox proportional hazard analysis (fit among the total sample, including non-survivors) revealed that improved compliance of the respiratory system and oxygenation were associated with the probability of extubation. Oxygenation improved at a lower rate in the subjects with COVID-19-associated ARDS than in the subjects with non-COVID ARDS. CONCLUSIONS: Mechanical ventilation duration was longer in subjects with COVID-19-associated ARDS compared with the subjects with non-COVID ARDS, which may be explained by a lower rate of improvement in oxygenation status.

The impact of COVID-19 on the value of coastal recreational resource from the perspective of recreational behaviour change: Evident from China.

The Corona Virus Disease 2019 (COVID-19) outbreak took a heavy toll on the global tourism industry in 2020, and affected the value realization of coastal recreational ecosystem service. From the micro perspective, this paper combines travel cost method with contingent behaviour method to obtain residents' actual behaviour and contingent behaviour data, and discusses the impact of the outbreak of COVID-19 on the value realization of coastal recreational resources from the perspective of the change in residents' recreational behaviour in Qingdao, China. Residents are observed to significantly reduce their outdoor activities in response to the COVID-19. The number of visits to the beach decreases by 25.2% when there is an outbreak, and decreases by 0.064% for every 1% increase in the number of confirmed cases, which is used to represent the severity of the epidemic. The asymmetries effects of epidemic situation on residents' recreational behaviour show that the improvements lead to larger and more significant impacts than the deteriorations. The disappearance of the pandemic crisis will provide considerable welfare for the citizens in Qingdao, which reaches to 1.9323 billion CNY/year. If the number of confirmed cases deteriorates to 900, the environmental welfare loss will be 0.3366 billion CNY/year. Additionally, we test the effects of residents' cognitive variables, and find that risk perception can strengthen the negative impacts of COVID-19 cases. Furthermore, the deteriorations in the environmental attributes are found to have stronger impacts on the number of visits than the improvements. This paper provides empirical-support results about the change of coastal recreational value through the evaluation of recreational behaviours in the post-epidemic period, which will give important implications for government's marine ecosystem restoration and coastal management work.

A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data

The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.

The Survival Probability of Covid-19 Patients with Type 2 Diabetes Mellitus during Pandemic at Al Ihsan Hospital, West Java Province, Indonesia

BACKGROUND: The high prevalence of diabetes mellitus (DM) in the population causes DM to become one of the most common comorbidities of coronavirus disease 2019 (COVID-19). Patients with diabetes have a higher risk of experiencing serious complications from COVID-19 and even death. AIM: This study was aimed to determine the difference in survival probability of COVID-19 patients, based on their DM status and to determine the association between type 2 DM and COVID-19 mortality at Al Ihsan Hospital, West Java Province, Indonesia. METHOD(S): The population of this retrospective cohort study were COVID-19 patients, aged >=18 years and were treated at Al Ihsan Hospital, from March 2020 to December 31, 2021. Differences in survival probability were obtained from survival analysis with Kaplan-Meier. Cox Proportional Hazard regression was used to determine the association between type 2 DM and COVID-19 mortality. RESULT(S): Totally, 308 confirmed positive COVID-19 patients were recruited in this study. During the 21 days of observation, survival probability of COVID-19 patients with type 2 DM was significantly lower than those without type 2 DM (71.24% vs. 84.13% respectively, with p = 0.0056). There was a statistically significant association between type 2 DM and COVID-19 mortality after controlling for age, cough symptoms, acute respiratory distress syndrome, vaccination, chronic kidney disease, ventilator use, antiviral therapy, and the percentage of bed occupation rate COVID-19 isolation at admission. The adjusted hazard ratio showing association between type 2 DM and COVID-19 mortality in the final model of multivariate analysis was 2.68 (95% CI 1.24-5.73). CONCLUSION(S): The survival probability of COVID-19 patients with type 2 DM was significantly lower than those without type 2 DM. COVID-19 patients with DM in Al Ihsan Hospital were almost 3 times more likely to be fatal as compared COVID-19 patients without DM.Copyright © 2023 Oka Septiriani, Mondastri Korib Sudaryo, Syahrizal Syarif, Citra Citra.

Screening compounds for treating the diabetes and COVID-19 from Miao medicine by molecular docking and bioinformatics.

Both diabetes and Corona Virus Disease 2019 (COVID-19) are seriously harmful to human health, and they are closely related. It is of great significance to find drugs that can simultaneously treat diabetes and COVID-19. Based on the theory of traditional Chinese medicine for treating COVID-19, this study first sorted out the compounds of Guizhou Miao medicine with "return to the lung channel" and "clear heat and detoxify" effects in China. The active components against COVID-19 were screened by molecular docking with SARS-CoV-2 PLpro and angiotensin-converting enzyme II as targets. Furthermore, the common target dipeptidyl peptidase 4 (DPP4) of diabetes and COVID-19 was used as a screening protein, and molecular docking was used to obtain potential components for the treatment of diabetes and COVID-19. Finally, the mechanism of potential ingredients in the treatment of diabetes and COVID-19 was explored with bioinformatics. More than 80 kinds of Miao medicine were obtained, and 584 compounds were obtained. Further, 110 compounds against COVID-19 were screened, and top 6 potential ingredients for the treatment of diabetes and COVID-19 were screened, including 3-O-ß-D-Xylopyranosyl-(1-6)-ß-D-glucopyranosyl-(1-6)-ß-D-glucopyranosyl oleanolic acid 28-O-ß-D-glucopyranosyl ester, Glycyrrhizic acid, Sequoiaflavone, 2-O-Caffeoyl maslinic acid, Pholidotin, and Ambewelamide A. Bioinformatics analysis found that their mechanism of action in treating diabetes and COVID-19 may be related to regulating the expression of DPP4, angiotensin II type 1 receptor, vitamin D receptor, plasminogen, chemokine C-C-motif receptor 6, and interleukin 2. We believe that Guizhou Miao medicine is rich in potential ingredients for the treatment of diabetes and COVID-19.

Characteristics of Traditional Chinese Medicine Syndromes and Treatments of COVID-19 Patients from Two Hospitals Based on "Treatment of Disease in Accordance with Three Conditions".

Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method(s): Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result(s): A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion(s): The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Automatic Diagnosis of COVID-19 Based on Machine Learning

The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.

Towards Neuro-CoViD-19.

CoViD-19 is the current pandemic caused by the Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2). Infection by SARS-CoV-2 occurs via the binding of its S protein to the angiotensin-converting enzyme-2 receptor (ACE2-R). S binding to ACE2-R leads to a drop in ACE2, a homolog of angiotensin converting enzyme (ACE). In the central nervous system (CNS), ACE mediates neuroinflammation, neurodegeneration and neurotoxicity responsible for several CNS disorders. ACE2 counteracts the damaging effects of ACE on CNS neurons. SARS-CoV-2 can directly access the CNS via the circulation or via cranial nerve I and the olfactory bulb. Inactivation of ACE2 following binding of SARS-CoV-2 S protein to ACE2-R in situ might blunt ACE2-moderating effects upon ACE CNS neurotoxicity and neurodegeneration. Here, we propose a neurobiological mechanism directly involving SARS-CoV-2 binding to ACE2-R in the etiology of putative Neuro-CoViD-19.

[Features of Production Process and Risk Prevention and Control Points of Corona Virus Disease 2019 (2019-nCoV) Antigen Detection Reagent (Colloidal Gold Method)].

Corona Virus Disease 2019 (2019-nCoV) antigen detection reagent (colloidal gold method) has been applied to people who go to basic medical and health institutions for medical treatment and have respiratory tract, fever and other symptoms within 5 days, isolate observers, community residents who need antigen self-testing. The wide application of the reagent can effectively shorten the detection time, reduce the detection cost and time cost, and alleviate the pressure of nucleic acid detection. The article details the structural components, testing principles, production process and key risk points of the new coronavirus antigen test reagents, with the aim of providing a reference for the development of relevant work specifications for manufacturers, the organization of safe production and the verification and supervision of regulatory authorities.

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.

Since the year 2019, the entire world has been facing the most hazardous and contagious disease as Corona Virus Disease 2019 (COVID-19). Based on the symptoms, the virus can be identified and diagnosed. Amongst, cough is the primary syndrome to detect COVID-19. Existing method requires a long processing time. Early screening and detection is a complex task. To surmount the research drawbacks, a novel ensemble-based deep learning model is designed on heuristic development. The prime intention of the designed work is to detect COVID-19 disease using cough audio signals. At the initial stage, the source signals are fetched and undergo for signal decomposition phase by Empirical Mean Curve Decomposition (EMCD). Consequently, the decomposed signal is called "Mel Frequency Cepstral Coefficients (MFCC), spectral features, and statistical features". Further, all three features are fused and provide the optimal weighted features with the optimal weight value with the help of "Modified Cat and Mouse Based Optimizer (MCMBO)". Lastly, the optimal weighted features are fed as input to the Optimized Deep Ensemble Classifier (ODEC) that is fused together with various classifiers such as "Radial Basis Function (RBF), Long-Short Term Memory (LSTM), and Deep Neural Network (DNN)". In order to attain the best detection results, the parameters in ODEC are optimized by the MCMBO algorithm. Throughout the validation, the designed method attains 96% and 92% concerning accuracy and precision. Thus, result analysis elucidates that the proposed work achieves the desired detective value that aids practitioners to early diagnose COVID-19 ailments.

Factors Predictive of Development of Acute Kidney Injury in Patients With COVID-19 in Brunei Darussalam.

INTRODUCTION: Acute kidney injury (AKI) in coronavirus disease 2019 (COVID-19) patients affects their health outcomes. Incidence and outcomes varied in the literature, particularly with different population and epidemiological demographics. Data remain scarce in the Southeast Asia region. We report the incidence, outcomes, pattern, types of AKI, and factors that influence AKI patient outcomes in Brunei Darussalam. METHODS: All patients (N = 930) with COVID-19 who were admitted to the National Isolation Center (between 7th August 2021 and 30thSeptember 2021) were included in the study. The confirmation of AKI was based on the KDIGO (Kidney Disease Improving Global Outcomes) criteria. RESULTS: The mean age of the patients was 41.9 ± 14.4 years with diabetes mellitus (DM), hypertension (HT), and chronic kidney disease (CKD) accounting for 11.7%, 29.1%, and 4.8% of comorbidities, respectively. Overall, 109 (11.7%) had AKI (KDIGO Stage 1 [67.9%], 2 [13.8%], and 3 [18.3%]), while 75.2% of the cases occurred pre-admission and 26.6% were cases of acute exacerbation of CKD. Univariate analysis identified age (odd ratio [OR] 1.06), male gender (OR 1.63), local nationality (OR 8.03), DM (OR 4.44), HT (OR 5.29), vascular disease (OR 6.08), presence of gastrointestinal symptoms (OR 2.08), antibiotic (OR 3.70) and nephrotoxins exposures (OR 8.57) as significant variables. Multivariate analysis showed age (adjusted OR [AOR] 1.04), male gender (AOR 1.67), gastrointestinal symptoms (AOR 1.61), antibiotic (AOR 2.34), and nephrotoxins exposure (AOR 4.73) as significant. CONCLUSIONS: Our study showed that one in nine patients with COVID-19 developed AKI with almost a third having stages 2 and 3 AKI. Older age, male gender, presence of GI symptoms, and antibiotic and nephrotoxin exposures were significant predictors of AKI. Patients with these factors should be prioritized for admission and treatment. Even though manifestations are generally now less severe, findings from this study can guide the management of COVID-19 as the disease enters the endemic stage. Furthermore, lessons learned from the COVID-19 pandemic will provide useful information and knowledge for future viral outbreaks or pandemics.

Comparison of Atmospheric Environment in China during Sars and Covid-19 Pandemics

Satellite remote sensing has advantages in monitoring environmental changes during the global pandemics such as the Severe Acute Respiratory Syndrome Coronavirus (SARS) and the Corona Virus Disease 2019 (COVID-19). In this paper, the variations of atmospheric environment during SARS and COVID-19 pandemics were calculated and analyzed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Monthly Global Product. Preliminary results show that: (1) aerosol optical depth is most affected by the pandemics, especially the duration and prevention and control measures;(2) the correlations between the variables of aerosol optical depth, cloud fraction, total column ozone and precipitable water vapor were not very strong during the two pandemics.

Co-occurrence of CML Blast Crisis and Severe COVID 19 Infection: A Case Report.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affecting multiple organ systems. It can cause severe cytokine storms leading to intensive care unit admission requiring mechanical ventilation. However, there have been few studies establishing the outcomes of chronic myeloid leukemia (CML) patients on tyrosine kinase inhibitors who are infected with COVID-19. We present a 69-year-old male with a history of CML on imatinib therapy with COVID-19 who developed acute respiratory distress syndrome needing mechanical ventilatory support, shock requiring vasopressors, and worse outcome secondary to blast crisis.

Topical review on nano-vaccinology: Biochemical promises and key challenges.

Nanomaterials have wide-ranging biomedical applications in prevention, treatment and control of diseases. Nanoparticle based vaccines have proven prodigious prophylaxis of various infectious and non-infectious diseases of human and animal concern. Nano-vaccines outnumber the conventional vaccines by virtue of plasticity in physio-chemical properties and ease of administration. The efficacy of nano-based vaccines may be attributed to the improved antigen stability, minimum immuno-toxicity, sustained release, enhanced immunogenicity and the flexibility of physical features of nanoparticles. Based on these, the nano-based vaccines have potential to evoke both cellular and humoral immune responses. Targeted and highly specific immunological pathways required for solid and long lasting immunity may be achieved with specially engineered nano-vaccines. This review presents an insight into the prevention of infectious diseases (of bacterial, viral and parasitic origin) and non-infectious diseases (cancer, auto-immune diseases) using nano-vaccinology. Additionally, key challenges to the effective utilization of nano-vaccines from bench to clinical settings have been highlighted as research domains for future.

Sentiment Analysis and Topic Classification of Twitter-based Data Set on the Face-to-face Classes Resumption in the Philippines during the COVID-19 Pandemic

Due to the Corona Virus Disease 2019 (COVID-19) pandemic, there was a need for shift in pedagogy of education. Several delivery modes for educational materials and activities had to be implemented to adapt in the situation brought about by the pandemic. In the Philippines, there has been a call to fully transition to face-to-face classes expressed on social media. In this study, a data set was built consisting of tweets (Twitter data) regarding the resumption of face-to-face classes in the Philippines. This data set was subjected to training and testing to classify them in terms of topic and sentiment using Recurrent Neural Network Long Short-Term Memory (LSTM) and Multinomial Naïve Bayes. The LSTM sentiment classifier resulted to 78.33% accuracy and LSTM topic classifier produced 61.34% accuracy. The Multinomial Naïve Bayes classifier obtained 77.22% accuracy for classifying sentiment while 58.33% accuracy for topic classification. © 2022 IEEE.